The primary difference between Q-learning and SARSA lies in the way they update Q-values.
- Q-learning is an off-policy algorithm, meaning it updates Q-values using the best possible action in the next state, regardless of the action the agent actually takes. This allows Q-learning to learn the optimal policy even if the agent is not following it.
- SARSA, on the other hand, is an on-policy algorithm. It updates Q-values based on the actual action taken in the next state, reflecting the agent’s real behavior rather than an idealized version of it.
This difference has important implications for exploration and exploitation. Q-learning tends to perform better in environments where the agent can explore more efficiently, as it optimizes for the best possible action. SARSA, being on-policy, tends to be more conservative, as it evaluates actions based on the agent’s current policy, including exploration.